Reduced Particle in Cell method for the Vlasov-Poisson system using auto-encoder and Hamiltonian neural

TL;DR

This paper presents a novel data-driven model reduction technique for plasma simulations that combines autoencoders and Hamiltonian neural networks to efficiently approximate the Vlasov-Poisson system while preserving its Hamiltonian structure.

Contribution

It introduces a two-step nonlinear reduction method using Proper Symplectic Decomposition and autoencoders, coupled with Hamiltonian neural networks, for the first time applied to plasma dynamics.

Findings

Outperforms standard linear Hamiltonian reduction methods.

Accurately captures plasma phenomena like Landau damping.

Reduces computational costs significantly.

Abstract

Hamiltonian particle-based simulations of plasma dynamics are inherently computationally intensive, primarily due to the large number of particles required to obtain accurate solutions. This challenge becomes even more acute in many-query contexts, where numerous simulations must be conducted across a range of time and parameter values. Consequently, it is essential to construct reduced order models from such discretizations to significantly lower computational costs while ensuring validity across the specified time and parameter domains. Preserving the Hamiltonian structure in these reduced models is also crucial, as it helps maintain long-term stability. In this paper, we introduce a nonlinear, non-intrusive, data-driven model order reduction method for the 1D-1V Vlasov--Poisson system, discretized using a Hamiltonian Particle-In-Cell scheme. Our approach relies on a two-step projection framework: an initial linear projection based on the Proper Symplectic Decomposition, followed by a nonlinear projection learned via an autoencoder neural network. The reduced dynamics are then modeled using a Hamiltonian neural network. The offline phase of the method is split into two stages: first, constructing the linear projection using full-order model snapshots; second, jointly training the autoencoder and the Hamiltonian neural network to simultaneously learn the encoder-decoder mappings and the reduced dynamics. We validate the proposed method on several benchmarks, including Landau damping and two-stream instability. The results show that our method has better reduction properties than standard linear Hamiltonian reduction methods.